CNN-LSTM-based Prediction of the Anode Effect in Aluminum Electrolytic Cell-Reference-Cited by-同舟云学术

CNN-LSTM-based Prediction of the Anode Effect in Aluminum Electrolytic Cell

Published:2022-07-25 Issue: Volume: Page:
ISSN:
Container-title:2022 41st Chinese Control Conference (CCC)
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Author:

Chao Wang¹,Qing Li¹,Jiarui Cui¹,Zihan Wang¹,Li Ma²

Affiliation:

1. School of Automation And Electeical Engineering, University of Science and Technology Beijing,Beijing,China

2. Beijing Aerospace Petrochemical Technology & Equipment Engineering Corporation Limited,Beijing,China

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9901509/9901494/09902505.pdf?arnumber=9902505

Reference11 articles.

1. XGBoost-based on-line prediction of seam tensile strength for Al-Li alloy in laser welding: Experiment study and modelling

2. Anode effect prediction based on support vector machine and K nearest neighbor[C]//2017 Chinese Automation Congress (CAC);zhou;IEEE,2017

3. Anode Effect prediction based on Expectation Maximization and XGBoost model[C]//2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS);zhang;IEEE,2018

4. A CNN-LSTM Hybrid Model Based Short-term Power Load Forecasting

5. Anode Effect Prediction;kremser;Hall-Héroult Cells Using Time Series Characteristics[J] Applied Sciences,2020

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